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Matej Kristan

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11 papers

2 author rows

AAAI Conference 2026 Conference Paper

Generalized-Scale Object Counting with Gradual Query Aggregation

Jer Pelhan
Alan Lukežič
Matej Kristan

Few-shot detection-based counters estimate the number of instances in the image specified only by a few test-time exemplars. A common approach to localize objects across multiple sizes is to merge backbone features of different resolutions. Furthermore, to enable small object detection in densely populated regions, the input image is commonly upsampled and tiling is applied to cope with the increased computational and memory requirements. Because of these ad-hoc solutions, existing counters struggle with images containing diverse-sized objects and densely populated regions of small objects. We propose GeCo2, an end-to-end few-shot counting and detection method that explicitly addresses the object scale issues. A new dense query representation gradually aggregates exemplar-specific feature information across scales that leads to high-resolution dense queries that enable detection of large as well as small objects. GeCo2 surpasses state-of-the-art few-shot counters in counting as well as detection accuracy by ~10% while running ~3x faster at smaller GPU memory footprint.

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NeurIPS Conference 2024 Conference Paper

A Novel Unified Architecture for Low-Shot Counting by Detection and Segmentation

Jer Pelhan
Alan Lukežič
Vitjan Zavrtanik
Matej Kristan

Low-shot object counters estimate the number of objects in an image using few or no annotated exemplars. Objects are localized by matching them to prototypes, which are constructed by unsupervised image-wide object appearance aggregation. Due to potentially diverse object appearances, the existing approaches often lead to overgeneralization and false positive detections. Furthermore, the best-performing methods train object localization by a surrogate loss, that predicts a unit Gaussian at each object center. This loss is sensitive to annotation error, hyperparameters and does not directly optimize the detection task, leading to suboptimal counts. We introduce GeCo, a novel low-shot counter that achieves accurate object detection, segmentation, and count estimation in a unified architecture. GeCo robustly generalizes the prototypes across objects appearances through a novel dense object query formulation. In addition, a novel counting loss is proposed, that directly optimizes the detection task and avoids the issues of the standard surrogate loss. GeCo surpasses the leading few-shot detection-based counters by $\sim$25\% in the total count MAE, achieves superior detection accuracy and sets a new solid state-of-the-art result across all low-shot counting setups. The code will be available on GitHub.

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IROS Conference 2022 Conference Paper

Temporal Context for Robust Maritime Obstacle Detection

Lojze Zust
Matej Kristan

Robust maritime obstacle detection is essential for fully autonomous unmanned surface vehicles (USVs). The currently widely adopted segmentation-based obstacle detection methods are prone to misclassification of object reflections and sun glitter as obstacles, producing many false positive detections, effectively rendering the methods impractical for USV navigation. However, water-turbulence-induced temporal appearance changes on object reflections are very distinctive from the appearance dynamics of true objects. We harness this property to design WaSR-T, a novel maritime obstacle detection network, that extracts the temporal context from a sequence of recent frames to reduce ambiguity. By learning the local temporal characteristics of object reflection on the water surface, WaSR-T substantially improves obstacle detection accuracy in the presence of reflections and glitter. Compared with existing single-frame methods, WaSR-T reduces the number of false positive detections by 41% overall and by over 53% within the danger zone of the boat, while preserving a high recall, and achieving new state-of-the-art performance on the challenging MODS maritime obstacle detection benchmark. The code, pretrained models and extended datasets are available at: https://github.com/lojzezust/WaSR-T

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EAAI Journal 2020 Journal Article

A segmentation-based approach for polyp counting in the wild

Vitjan Zavrtanik
Martin Vodopivec
Matej Kristan

We address the problem of jellyfish polyp counting in underwater images. Modern methods utilize convolutional neural networks for feature extraction and work in two stages. First, hypothetical regions are proposed at potential locations, the features of the regions are extracted and classified according to the contained object. Such methods typically require a dense grid for region proposals, explicitly test various scales and are prone to failure in densely populated regions. We propose a segmentation-based polyp counter — SegCo. A convolutional neural network is trained to produce locally-circular segmentation masks on the polyps, which are then detected by localizing circularly symmetric areas in the segmented image. Detection stage is efficient and avoids a greedy search over position and scales. SegCo outperforms the current state-of-the-art object detector RetinaNet (Lin et al. , 2017) and the recent specialized polyp detection method PoCo (Vodopivec et al. , 2018) by 2% and 24% in F-score, respectively, and sets a new state-of-the-art in polyp detection.

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ICRA Conference 2020 Conference Paper

A water-obstacle separation and refinement network for unmanned surface vehicles

Borja Bovcon
Matej Kristan

Obstacle detection by semantic segmentation shows a great promise for autonomous navigation in unmanned surface vehicles (USV). However, existing methods suffer from poor estimation of the water edge in presence of visual ambiguities, poor detection of small obstacles and high false-positive rate on water reflections and wakes. We propose a new deep encoder-decoder architecture, a water-obstacle separation and refinement network (WaSR), to address these issues. Detection and water edge accuracy are improved by a novel decoder that gradually fuses inertial information from inertial measurement unit (IMU) with the visual features from the encoder. In addition, a novel loss function is designed to increase the separation between water and obstacle features early on in the network. Subsequently, the capacity of the remaining layers in the decoder is better utilised, leading to a significant reduction in false positives and increased true positives. Experimental results show that WaSR outperforms the current state-of-the-art by a large margin, yielding a 14% increase in F-measure over the second-best method.

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IROS Conference 2019 Conference Paper

The MaSTr1325 dataset for training deep USV obstacle detection models

Borja Bovcon
Jon Muhovic
Janez Pers
Matej Kristan

The progress of obstacle detection via semantic segmentation on unmanned surface vehicles (USVs) has been significantly lagging behind the developments in the related field of autonomous cars. The reason is the lack of large curated training datasets from USV domain required for development of data-hungry deep CNNs. This paper addresses this issue by presenting MaSTr1325, a marine semantic segmentation training dataset tailored for development of obstacle detection methods in small-sized coastal USVs. The dataset contains 1325 diverse images captured over a two year span with a real USV, covering a range of realistic conditions encountered in a coastal surveillance task. The images are per-pixel semantically labeled. The dataset exceeds previous attempts in this domain in size, scene complexity and domain realism. In addition, a dataset augmentation protocol is proposed to address slight appearance differences of the images in the training set and those in deployment. The accompanying experimental evaluation provides a detailed analysis of popular deep architectures, annotation accuracy and influence of the training set size. MaSTr1325 will be released to reaserch community to facilitate progress in obstacle detection for USVs.

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IROS Conference 2018 Conference Paper

Obstacle Detection for USVs by Joint Stereo-View Semantic Segmentation

Borja Bovcon
Matej Kristan

We propose a stereo-based obstacle detection approach for unmanned surface vehicles. Obstacle detection is cast as a scene semantic segmentation problem in which pixels are assigned a probability of belonging to water or non-water regions. We extend a single-view model to a stereo system by adding a constraint which prefers consistent class labels assignment to pixels in the left and right camera images corresponding to the same parts of a 3D scene. Our approach jointly fits a semantic model to both images, leading to an improved class-label posterior map from which obstacles and water edge are extracted. In overall F-measure, our approach outperforms the current state-of-the-art monocular approach by 0. 495, a monocular CNN by 0. 798 and their stereo extensions by 0. 059 and 0. 515, respectively on the task of obstacle detection while running real-time on a single CPU.

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ICRA Conference 2016 Conference Paper

Hierarchical spatial model for 2D range data based room categorization

Peter Ursic
Ales Leonardis
Danijel Skocaj
Matej Kristan

The next generation service robots are expected to co-exist with humans in their homes. Such a mobile robot requires an efficient representation of space, which should be compact and expressive, for effective operation in real-world environments. In this paper we present a novel approach for 2D ground-plan-like laser-range-data-based room categorization that builds on a compositional hierarchical representation of space, and show how an additional abstraction layer, whose parts are formed by merging partial views of the environment followed by graph extraction, can achieve improved categorization performance. A new algorithm is presented that finds a dictionary of exemplar elements from a multi-category set, based on the affinity measure defined among pairs of elements. This algorithm is used for part selection in new layer construction. Room categorization experiments have been performed on a challenging publicly available dataset, which has been extended in this work. State-of-the-art results were obtained by achieving the most balanced performance over all categories.

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ICRA Conference 2016 Conference Paper

Part-based room categorization for household service robots

Peter Ursic
Rok Mandeljc
Ales Leonardis
Matej Kristan

A service robot that operates in a previously-unseen home environment should be able to recognize the functionality of the rooms it visits, such as a living room, a bathroom, etc. We present a novel part-based model and an approach for room categorization using data obtained from a visual sensor. Images are represented with sets of unordered parts that are obtained by object-agnostic region proposals, and encoded using state-of-the-art image descriptor extractor - a convolutional neural network (CNN). An approach is proposed that learns category-specific discriminative parts for the part-based model. The proposed approach was compared to the state-of-the-art CNN trained specifically for place recognition. Experimental results show that the proposed approach outperforms the holistic CNN by being robust to image degradation, such as occlusions, modifications of image scaling, and aspect changes. In addition, we report non-negligible annotation errors and image duplicates in a popular dataset for place categorization and discuss annotation ambiguities.

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IROS Conference 2012 Conference Paper

Room classification using a hierarchical representation of space

Peter Ursic
Matej Kristan
Danijel Skocaj
Ales Leonardis

Mobile robots need an effective spatial model for the successful operation in real-world environment. The model should be compact and simultaneously possess large expressive power. Moreover, it should scale well. In this paper we propose a new hierarchical representation of space, whose compositional structure is learned based on statistically significant observations. We have focused on a two dimensional space, since many robots perceive their surroundings in two dimensions with the use of a laser range finder or a sonar. We also propose the use of a low-level image descriptor for addressing the room classification problem, by which we demonstrate the performance of our representation. Using only the lower layers of the hierarchy, we obtain state-of-the-art classification results on demanding datasets.

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IROS Conference 2011 Conference Paper

A system for interactive learning in dialogue with a tutor

Danijel Skocaj
Matej Kristan
Alen Vrecko
Marko Mahnic
Miroslav Janícek
Geert-Jan M. Kruijff
Marc Hanheide
Nick Hawes

In this paper we present representations and mechanisms that facilitate continuous learning of visual concepts in dialogue with a tutor and show the implemented robot system. We present how beliefs about the world are created by processing visual and linguistic information and show how they are used for planning system behaviour with the aim at satisfying its internal drive - to extend its knowledge. The system facilitates different kinds of learning initiated by the human tutor or by the system itself. We demonstrate these principles in the case of learning about object colours and basic shapes.

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